One computer implemented approach for calculating a demand forecast involves defining a so-called demand forecast tree capable of being graphically represented by a single top level node with at least two branches directly emanating therefrom, each branch having at least one further node. The demand forecast is computed on the basis of historical sales data typically associated with bottom level nodes of a demand forecast tree by a forecast engine capable of determining a mathematical simulation model for a demand process. One such forecast engine employing statistical seasonal causal time series models of count data is commercially available from Demantra Ltd, Israel, under the name Demantra™ Demand Planner.
One exemplary demand forecast application is the media distribution problem, namely, determining the number of copies of different daily newspapers to be delivered daily to different locations to minimize two mutually conflicting indices commonly quantified for evaluating the efficacy of a distribution policy for a newspaper: the frequency of sellouts, and the number of returns both typically expressed in percentage terms. It is common practice in the industry that a draw for a newspaper at a location for a given day is greater than its demand forecast at that location for that day so as to reduce the probability of a sellout but with the inherent penalty that returns will be greater. In the case of distribution policies for newspapers, safety stocks are allocated to locations to ensure a predetermined availability level for a given demand probability function to achieve a reasonable balance between expected returns and expected occurrences of sellouts. Moreover, it is common practice that locations are sorted into one of several classes depending on the average number of copies sold, each class being assigned a different availability level, say, 70%, 80%, and the like.